Introduction

This paper has two main aims. First, to give an introduction to some of the construction techniques – the ‘nuts-and-bolts’ as it were – of neural networks deployed by the authors in this book. Our intention is to emphasise conceptual principles and their associated terminology, and to do this wherever possible without recourse to detailed mathematical descriptions. However, the term ‘neural network’ has taken on a multitude of meanings over the last couple of decades, depending on its methodological and scientific context. A second aim, therefore, given that the application of the techniques described in this book may appear rather diverse, is to supply some meta-theoretical landmarks to help understand the significance of the ensuing results.

In general terms, neural networks are tools for building models of systems that are characterised by data sets which are often (but not always) derived by sampling a system input-output behaviour. While a neural network model is of some utility if it mimics the behaviour of the target system, it is far more useful if key mechanisms underlying the model functionality can be unearthed, and identified with those of the underlying system. That is, the modeller can ‘break into’ the model, viewed initially as an input-output ‘black box’, and find internal representations, variable relationships, and structures which may correspond with the underlying target system. This target system may be entirely non-biological (e.g. stock market prices), or be of biological origin, but have nothing to do with brains (e.g. ecologically driven patterns of population dynamics).